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The sound captured through a microphone is a rich information source that can be used to withdraw exact inferences about the speaker, its environment and social events. It is a modality that supports a wide set of features, such as conversation detection, activity recognizer, localization classification, social net structure discovery. However, it is traditionally used for its content, resorting to techniques like Automated Speech Recognition to understand what's said. But the sound itself, regardless of its semantics, can provide information. For example, in the course of a class, if the level of noise increases, this can indicate that some slide generated comments or that the students lost interest. This can be used to create a log of the student's interest in the several parts of the class, to improve it where needed. Similarly, in a meeting or lecture, it can indicate when a polemic subject was referred. If we go deeper, and analyse the sound's frequency, we can identify whena person is speaking and by analysing the timbre, we can recognize who spoke. Make More Noise!